Gotowe bibliografie tematyczne / Bayesian recovery

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Gotowa bibliografia na temat „Bayesian recovery”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Bayesian recovery"

Zhao, Juan, Xia Bai, Tao Shan, and Ran Tao. "Block Sparse Bayesian Recovery with Correlated LSM Prior." Wireless Communications and Mobile Computing 2021 (October 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/9942694.

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Compressed sensing can recover sparse signals using a much smaller number of samples than the traditional Nyquist sampling theorem. Block sparse signals (BSS) with nonzero coefficients occurring in clusters arise naturally in many practical scenarios. Utilizing the sparse structure can improve the recovery performance. In this paper, we consider recovering arbitrary BSS with a sparse Bayesian learning framework by inducing correlated Laplacian scale mixture (LSM) prior, which can model the dependence of adjacent elements of the block sparse signal, and then a block sparse Bayesian learning algorithm is proposed via variational Bayesian inference. Moreover, we present a fast version of the proposed recovery algorithm, which does not involve the computation of matrix inversion and has robust recovery performance in the low SNR case. The experimental results with simulated data and ISAR imaging show that the proposed algorithms can efficiently reconstruct BSS and have good antinoise ability in noisy environments.

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Calvetti, D., and E. Somersalo. "Recovery of shapes: hypermodels and Bayesian learning." Journal of Physics: Conference Series 124 (July 1, 2008): 012014. http://dx.doi.org/10.1088/1742-6596/124/1/012014.

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Gan, Wei, Lu-ping Xu, Zhe Su, and Hua Zhang. "Bayesian Hypothesis Testing Based Recovery for Compressed Sensing." Journal of Electronics & Information Technology 33, no. 11 (2011): 2640–46. http://dx.doi.org/10.3724/sp.j.1146.2011.00151.

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Long, Zhen, Ce Zhu, Jiani Liu, and Yipeng Liu. "Bayesian Low Rank Tensor Ring for Image Recovery." IEEE Transactions on Image Processing 30 (2021): 3568–80. http://dx.doi.org/10.1109/tip.2021.3062195.

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Korki, Mehdi, Hadi Zayyani, and Jingxin Zhang. "Bayesian Hypothesis Testing for Block Sparse Signal Recovery." IEEE Communications Letters 20, no. 3 (2016): 494–97. http://dx.doi.org/10.1109/lcomm.2016.2518169.

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Brooks, S. P., E. A. Catchpole, B. J. T. Morgan, and S. C. Barry. "On the Bayesian Analysis of Ring-Recovery Data." Biometrics 56, no. 3 (2000): 951–56. http://dx.doi.org/10.1111/j.0006-341x.2000.00951.x.

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Wang, Lu, Lifan Zhao, Guoan Bi, and Chunru Wan. "Hierarchical Sparse Signal Recovery by Variational Bayesian Inference." IEEE Signal Processing Letters 21, no. 1 (2014): 110–13. http://dx.doi.org/10.1109/lsp.2013.2292589.

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Huang, Kaide, Yao Guo, Xuemei Guo, and Guoli Wang. "Heterogeneous Bayesian compressive sensing for sparse signal recovery." IET Signal Processing 8, no. 9 (2014): 1009–17. http://dx.doi.org/10.1049/iet-spr.2013.0501.

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Ahmed, Irfan, Aftab Khan, Nasir Ahmad, NasruMinallah, and Hazrat Ali. "Speech Signal Recovery Using Block Sparse Bayesian Learning." Arabian Journal for Science and Engineering 45, no. 3 (2019): 1567–79. http://dx.doi.org/10.1007/s13369-019-04080-6.

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Zhang, Shuanghui, Yongxiang Liu, Xiang Li, and Guoan Bi. "Variational Bayesian Sparse Signal Recovery With LSM Prior." IEEE Access 5 (2017): 26690–702. http://dx.doi.org/10.1109/access.2017.2765831.

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Rozprawy doktorskie na temat "Bayesian recovery"

Tan, Xing. "Bayesian sparse signal recovery." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0041176.

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Karseras, Evripidis. "Hierarchical Bayesian models for sparse signal recovery and sampling." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/32102.

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This thesis builds upon the problem of sparse signal recovery from the Bayesian standpoint. The advantages of employing Bayesian models are underscored, with the most important being the ease at which a model can be expanded or altered; leading to a fresh class of algorithms. The thesis fills out several gaps between sparse recovery algorithms and sparse Bayesian models; firstly the lack of global performance guarantees for the latter and secondly what the signifying differences are between the two. These questions are answered by providing; a refined theoretical analysis and a new class of algorithms that combines the benefits from classic recovery algorithms and sparse Bayesian modelling. The said Bayesian techniques find application in tracking dynamic sparse signals, something impossible under the Kalman filter approach. Another innovation of this thesis are Bayesian models for signals whose components are known a priori to exhibit a certain statistical trend. These situations require that the model enforces a given statistical bias on the solutions. Existing Bayesian models can cope with this input, but the algorithms to carry out the task are computationally expensive. Several ways are proposed to remedy the associated problems while still attaining some form of optimality. The proposed framework finds application in multipath channel estimation with some very promising results. Not far from the same area lies that of Approximate Message Passing. This includes extremely low-complexity algorithms for sparse recovery with a powerful analysis framework. Some results are derived, regarding the differences between these approximate methods and the aforementioned models. This can be seen as preliminary work for future research. Finally, the thesis presents a hardware implementation of a wideband spectrum analyser based on sparse recovery methods. The hardware consists of a Field-Programmable Gate Array coupled with an Analogue to Digital Converter. Some critical results are drawn, regarding the gains and viability of such methods.

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Echavarria, Gregory Maria Angelica. "Predictive Data-Derived Bayesian Statistic-Transport Model and Simulator of Sunken Oil Mass." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/471.

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Sunken oil is difficult to locate because remote sensing techniques cannot as yet provide views of sunken oil over large areas. Moreover, the oil may re-suspend and sink with changes in salinity, sediment load, and temperature, making deterministic fate models difficult to deploy and calibrate when even the presence of sunken oil is difficult to assess. For these reasons, together with the expense of field data collection, there is a need for a statistical technique integrating limited data collection with stochastic transport modeling. Predictive Bayesian modeling techniques have been developed and demonstrated for exploiting limited information for decision support in many other applications. These techniques brought to a multi-modal Lagrangian modeling framework, representing a near-real time approach to locating and tracking sunken oil driven by intrinsic physical properties of field data collected following a spill after oil has begun collecting on a relatively flat bay bottom. Methods include (1) development of the conceptual predictive Bayesian model and multi-modal Gaussian computational approach based on theory and literature review; (2) development of an object-oriented programming and combinatorial structure capable of managing data, integration and computation over an uncertain and highly dimensional parameter space; (3) creating a new bi-dimensional approach of the method of images to account for curved shoreline boundaries; (4) confirmation of model capability for locating sunken oil patches using available (partial) real field data and capability for temporal projections near curved boundaries using simulated field data; and (5) development of a stand-alone open-source computer application with graphical user interface capable of calibrating instantaneous oil spill scenarios, obtaining sets maps of relative probability profiles at different prediction times and user-selected geographic areas and resolution, and capable of performing post-processing tasks proper of a basic GIS-like software. The result is a predictive Bayesian multi-modal Gaussian model, SOSim (Sunken Oil Simulator) Version 1.0rc1, operational for use with limited, randomly-sampled, available subjective and numeric data on sunken oil concentrations and locations in relatively flat-bottomed bays. The SOSim model represents a new approach, coupling a Lagrangian modeling technique with predictive Bayesian capability for computing unconditional probabilities of mass as a function of space and time. The approach addresses the current need to rapidly deploy modeling capability without readily accessible information on ocean bottom currents. Contributions include (1) the development of the apparently first pollutant transport model for computing unconditional relative probabilities of pollutant location as a function of time based on limited available field data alone; (2) development of a numerical method of computing concentration profiles subject to curved, continuous or discontinuous boundary conditions; (3) development combinatorial algorithms to compute unconditional multimodal Gaussian probabilities not amenable to analytical or Markov-Chain Monte Carlo integration due to high dimensionality; and (4) the development of software modules, including a core module containing the developed Bayesian functions, a wrapping graphical user interface, a processing and operating interface, and the necessary programming components that lead to an open-source, stand-alone, executable computer application (SOSim - Sunken Oil Simulator). Extensions and refinements are recommended, including the addition of capability for accepting available information on bathymetry and maybe bottom currents as Bayesian prior information, the creation of capability of modeling continuous oil releases, and the extension to tracking of suspended oil (3-D).

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Tang, Man. "Bayesian population dynamics modeling to guide population restoration and recovery of endangered mussels in the Clinch River, Tennessee and Virginia." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/49598.

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Freshwater mussels have played an important role in the history of human culture and also in ecosystem functioning. But during the past several decades, the abundance and diversity of mussel species has declined all over the world. To address the urgent need to maintain and restore populations of endangered freshwater mussels, quantitative population dynamics modeling is needed to evaluate population status and guide the management of endangered freshwater mussels. One endangered mussel species, the oyster mussel (Epioblasma capsaeformis), was selected to study its population dynamics for my research. The analysis was based on two datasets, length frequency data from annual surveys conducted at three sites in Clinch River: Wallen Bend (Clinch River Mile 192) from 2004-2010, Frost Ford (CRM 182) from 2005 to 2010 and Swan Island (CRM 172) from 2005 to 2010, and age-length data based on shell thin-sections. Three hypothetical scenarios were assumed in model estimations: (1) constant natural mortality; (2) one constant natural mortality rate for young mussels and another one for adult mussels; (3) age-specific natural mortality. A Bayesian approach was used to analyze the age-structured models and a Bayesian model averaging approach was applied to average the results by weighting each model using the deviance information criterion (DIC). A risk assessment was conducted to evaluate alternative restoration strategies for E. capsaeformis. The results indicated that releasing adult mussels was the quickest way to increase mussel population size and increasing survival and fertility of young mussels was a suitable way to restore mussel populations in the long term. The population of E. capsaeformis at Frost Ford had a lower risk of decline compared with the populations at Wallen Bend and Swan Island. Passive integrated transponder (PIT) tags were applied in my fieldwork to monitor the translocation efficiency of E. capsaeformis and Actinonaias pectorosa at Cleveland Islands (CRM 270.8). Hierarchical Bayesian models were developed to address the individual variability and sex-related differences in growth. In model selection, the model considering individual variability and sex-related differences (if a species has sexual dimorphism) yielded the lowest DIC value. The results from the best model showed that the mean asymptotic length and mean growth rate of female E. capsaeformis were 45.34 mm and 0.279, which were higher than values estimated for males (42.09 mm and 0.216). The mean asymptotic length and mean growth rate for A. pectorosa were 104.2 mm and 0.063, respectively. To test for the existence of individual and sex-related variability in survival and recapture rates, Bayesian models were developed to address the variability in the analysis of the mark-recapture data of E. capsaeformis and A. pectorosa. DIC was used to compare different models. The median survival rates of male E. capsaeformis, female E. capsaeformis and A. pectorosa were high (>87%, >74% and >91%), indicating that the habitat at Cleveland Islands was suitable for these two mussel species within this survey duration. In addition, the median recapture rates for E. capsaeformis and A. pectorosa were >93% and >96%, indicating that the PIT tag technique provided an efficient monitoring approach. According to model comparison results, the non-hierarchical model or the model with sex--related differences (if a species is sexually dimorphic) in survival rate was suggested for analyzing mark-recapture data when sample sizes are small. Master of Science

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Cave, Vanessa M. "Statistical models for the long-term monitoring of songbird populations : a Bayesian analysis of constant effort sites and ring-recovery data." Thesis, St Andrews, 2010. http://hdl.handle.net/10023/885.

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Dine, James. "A habitat suitability model for Ricord's iguana in the Dominican Republic." Connect to resource online, 2009. http://hdl.handle.net/1805/1889.

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Thesis (M.S.)--Indiana University, 2009. Title from screen (viewed on August 27, 2009). Department of Geography, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Jan Ramer, Aniruddha Banergee, Jeffery Wilson. Includes vita. Includes bibliographical references (leaves 47-52).

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Sugimoto, Tatsuhiro. "Anelastic Strain Recovery Method for In-situ Stress Measurements： A novel analysis procedure based on Bayesian statistical modeling and application to active fault drilling." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263637.

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Chen, Cong. "High-Dimensional Generative Models for 3D Perception." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103948.

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Modern robotics and automation systems require high-level reasoning capability in representing, identifying, and interpreting the three-dimensional data of the real world. Understanding the world's geometric structure by visual data is known as 3D perception. The necessity of analyzing irregular and complex 3D data has led to the development of high-dimensional frameworks for data learning. Here, we design several sparse learning-based approaches for high-dimensional data that effectively tackle multiple perception problems, including data filtering, data recovery, and data retrieval. The frameworks offer generative solutions for analyzing complex and irregular data structures without prior knowledge of data. The first part of the dissertation proposes a novel method that simultaneously filters point cloud noise and outliers as well as completing missing data by utilizing a unified framework consisting of a novel tensor data representation, an adaptive feature encoder, and a generative Bayesian network. In the next section, a novel multi-level generative chaotic Recurrent Neural Network (RNN) has been proposed using a sparse tensor structure for image restoration. In the last part of the dissertation, we discuss the detection followed by localization, where we discuss extracting features from sparse tensors for data retrieval. Doctor of Philosophy The development of automation systems and robotics brought the modern world unrivaled affluence and convenience. However, the current automated tasks are mainly simple repetitive motions. Tasks that require more artificial capability with advanced visual cognition are still an unsolved problem for automation. Many of the high-level cognition-based tasks require the accurate visual perception of the environment and dynamic objects from the data received from the optical sensor. The capability to represent, identify and interpret complex visual data for understanding the geometric structure of the world is 3D perception. To better tackle the existing 3D perception challenges, this dissertation proposed a set of generative learning-based frameworks on sparse tensor data for various high-dimensional robotics perception applications: underwater point cloud filtering, image restoration, deformation detection, and localization. Underwater point cloud data is relevant for many applications such as environmental monitoring or geological exploration. The data collected with sonar sensors are however subjected to different types of noise, including holes, noise measurements, and outliers. In the first chapter, we propose a generative model for point cloud data recovery using Variational Bayesian (VB) based sparse tensor factorization methods to tackle these three defects simultaneously. In the second part of the dissertation, we propose an image restoration technique to tackle missing data, which is essential for many perception applications. An efficient generative chaotic RNN framework has been introduced for recovering the sparse tensor from a single corrupted image for various types of missing data. In the last chapter, a multi-level CNN for high-dimension tensor feature extraction for underwater vehicle localization has been proposed.

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SEDDA, GIULIA. "The interplay between movement and perception: how interaction can influence sensorimotor performance and neuromotor recovery." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1011732.

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Movement and perception interact continuously in daily activities. Motor output changes the outside world and affect perceptual representations. Similarly, perception has consequences on movement. Nevertheless, how movement and perception influence each other and share information is still an open question. Mappings from movement to perceptual outcome and vice versa change continuously throughout life. For example, a cerebrovascular accident (stroke) elicits in the nervous system a complex series of reorganization processes at various levels and with different temporal scales. Functional recovery after a stroke seems to be mediated by use-dependent reorganization of the preserved neural circuitry. The goal of this thesis is to discuss how interaction with the environment can influence the progress of both sensorimotor performance and neuromotor recovery. I investigate how individuals develop an implicit knowledge of the ways motor outputs regularly correlate with changes in sensory inputs, by interacting with the environment and experiencing the perceptual consequences of self-generated movements. Further, I applied this paradigm to model the exercise-based neurorehabilitation in stroke survivors, which aims at gradually improving both perceptual and motor performance through repeated exercise. The scientific findings of this thesis indicate that motor learning resolve visual perceptual uncertainty and contributes to persistent changes in visual and somatosensory perception. Moreover, computational neurorehabilitation may help to identify the underlying mechanisms of both motor and perceptual recovery, and may lead to more personalized therapies.

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Quer, Giorgio. "Optimization of Cognitive Wireless Networks using Compressive Sensing and Probabilistic Graphical Models." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3421992.

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In-network data aggregation to increase the efficiency of data gathering solutions for Wireless Sensor Networks (WSNs) is a challenging task. In the first part of this thesis, we address the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a Data Collection Point (DCP). We exploit Principal Component Analysis (PCA) to learn the relevant statistical characteristics of the signals of interest at the DCP. Then, at the DCP we use this knowledge to design a matrix required by the recovery techniques, that exploit convex optimization (Compressive Sensing, CS) in order to recover the whole signal sensed by the WSN from a small number of samples gathered. In order to integrate this monitoring model in a compression/recovery framework, we apply the logic of the cognition paradigm: we first observe the network, then we learn the relevant statistics of the signals, we apply it to recover the signal and to make decisions, that we effect through the control loop. This compression/recovery framework with a feedback control loop is named "Sensing, Compression and Recovery through ONline Estimation" (SCoRe1). The whole framework is designed for a WSN architecture, called WSN-control, that is accessible from the Internet. We also analyze with a Bayesian approach the whole framework to justify theoretically the choices made in our protocol design. The second part of the thesis deals with the application of the cognition paradigm to the optimization of a Wireless Local Area Network (WLAN). In this work, we propose an architecture for cognitive networking that can be integrated with the existing layered protocol stack. Specifically, we suggest the use of a probabilistic graphical model for modeling the layered protocol stack. In particular, we use a Bayesian Network (BN), a graphical representation of statistical relationships between random variables, in order to describe the relationships among a set of stack-wide protocol parameters and to exploit this cross-layer approach to optimize the network. In doing so, we use the knowledge learned from the observation of the data to predict the TCP throughput in a single-hop wireless network and to infer the future occurrence of congestion at the TCP layer in a multi-hop wireless network. The approach followed in the two main topics of this thesis consists of the following phases: (i) we apply the cognition paradigm to learn the specific probabilistic characteristics of the network, (ii) we exploit this knowledge acquired in the first phase to design novel protocol techniques, (iii) we analyze theoretically and through extensive simulation such techniques, comparing them with other state of the art techniques, and (iv) we evaluate their performance in real networking scenarios. La combinazione delle informazioni nelle reti di sensori wireless è una soluzione promettente per aumentare l'efficienza delle techiche di raccolta dati. Nella prima parte di questa tesi viene affrontato il problema della ricostruzione di segnali distribuiti tramite la raccolta di un piccolo numero di campioni al punto di raccolta dati (DCP). Viene sfruttato il metodo dell'analisi delle componenti principali (PCA) per ricostruire al DCP le caratteristiche statistiche del segnale di interesse. Questa informazione viene utilizzata al DCP per determinare la matrice richiesta dalle tecniche di recupero che sfruttano algoritmi di ottimizzazione convessa (Compressive Sensing, CS) per ricostruire l'intero segnale da una sua versione campionata. Per integrare questo modello di monitoraggio in un framework di compressione e recupero del segnale, viene applicata la logica del paradigma 'cognitive': prima si osserva la rete; poi dall'osservazione si derivano le statistiche di interesse, che vengono applicate per il recupero del segnale; si sfruttano queste informazioni statistiche per prenderere decisioni e infine si rendono effettive queste decisioni con un controllo in retroazione. Il framework di compressione e recupero con controllo in retroazione è chiamato "Sensing, Compression and Recovery through ONline Estimation" (SCoRe1). L'intero framework è stato implementato in una architettura per WSN detta WSN-control, accessibile da Internet. Le scelte nella progettazione del protocollo sono state giustificate da un'analisi teorica con un approccio di tipo Bayesiano. Nella seconda parte della tesi il paradigma cognitive viene utilizzato per l'ottimizzazione di reti locali wireless (WLAN). L'architetture della rete cognitive viene integrata nello stack protocollare della rete wireless. Nello specifico, vengono utilizzati dei modelli grafici probabilistici per modellare lo stack protocollare: le relazioni probabilistiche tra alcuni parametri di diversi livelli vengono studiate con il modello delle reti Bayesiane (BN). In questo modo, è possibile utilizzare queste informazioni provenienti da diversi livelli per ottimizzare le prestazioni della rete, utilizzando un approccio di tipo cross-layer. Ad esempio, queste informazioni sono utilizzate per predire il throughput a livello di trasporto in una rete wireless di tipo single-hop, o per prevedere il verificarsi di eventi di congestione in una rete wireless di tipo multi-hop. L'approccio seguito nei due argomenti principali che compongono questa tesi è il seguente: (i) viene applicato il paradigma cognitive per ricostruire specifiche caratteristiche probabilistiche della rete, (ii) queste informazioni vengono utilizzate per progettare nuove tecniche protocollari, (iii) queste tecniche vengono analizzate teoricamente e confrontate con altre tecniche esistenti, e (iv) le prestazioni vengono simulate, confrontate con quelle di altre tecniche e valutate in scenari di rete realistici.

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Książki na temat "Bayesian recovery"

Sawada, Tadamasa, Yunfeng Li, and Zygmunt Pizlo. Shape Perception. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.12.

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This chapter provides a review of topics and concepts that are necessary to study and understand 3D shape perception. This includes group theory and their invariants; model-based invariants; Euclidean, affine, and projective geometry; symmetry; inverse problems; simplicity principle; Fechnerian psychophysics; regularization theory; Bayesian inference; shape constancy and shape veridicality; shape recovery; perspective and orthographic projections; camera models; as well as definitions of shape. All concepts are defined and illustrated, and the reader is provided with references providing mathematical and computational details. Material presented here will be a good starting point for students and researchers who plan to study shape, as well as for those who simply want to get prepared for reading the contemporary literature on the subject.

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Części książek na temat "Bayesian recovery"

Kosarev, E. L. "Superresolution limit for Signal recovery." In Maximum Entropy and Bayesian Methods. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-7860-8_50.

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Grant, A. I., and K. J. Packer. "Enhanced Information Recovery From Spectroscopic Data Using MaxEnt." In Maximum Entropy and Bayesian Methods. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-7860-8_24.

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Yang, Haiyan, Xiaolin Huang, Cheng Peng, Jie Yang, and Li Li. "A New Bayesian Method for Jointly Sparse Signal Recovery." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70093-9_94.

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Zhou, Xuefeng, Hongmin Wu, Juan Rojas, Zhihao Xu, and Shuai Li. "Learning Policy for Robot Anomaly Recovery Based on Robot Introspection." In Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6263-1_6.

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Molina, Rafael, Aggelos K. Katsaggelos, and Javier Mateos. "Removal of Blocking Artifacts Using a Hierarchical Bayesian Approach." In Signal Recovery Techniques for Image and Video Compression and Transmission. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-6514-4_1.

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Chu, Chu, Guangjun Wen, Zhong Huang, Jian Su, and Yu Han. "Improved Bayesian Method with Collision Recovery for RFID Anti-collision." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24265-7_5.

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Huntbatch, Andrew, Su-Lin Lee, David Firmin, and Guang-Zhong Yang. "Bayesian Motion Recovery Framework for Myocardial Phase-Contrast Velocity MRI." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85990-1_10.

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Fu, Shunkai, Sein Minn, and Michel C. Desmarais. "Towards the Efficient Recovery of General Multi-Dimensional Bayesian Network Classifier." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08979-9_2.

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Wang, Lu, Lifan Zhao, Guoan Bi, and Xin Liu. "Alternative Extended Block Sparse Bayesian Learning for Cluster Structured Sparse Signal Recovery." In Wireless and Satellite Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19153-5_1.

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Pang, Tongyao, Yuhui Quan, and Hui Ji. "Self-supervised Bayesian Deep Learning for Image Recovery with Applications to Compressive Sensing." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58621-8_28.

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Streszczenia konferencji na temat "Bayesian recovery"

Niinimäki, Kati, Ville Kolehmainen, and Samuli Siltanen. "Bayesian Multiresolution Method for Local Tomography." In Signal Recovery and Synthesis. OSA, 2009. http://dx.doi.org/10.1364/srs.2009.stua2.

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Jalobeanu, André. "Bayesian Vision for Shape Recovery." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2004. http://dx.doi.org/10.1063/1.1835208.

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Pascazio, V., P. Mathieu, and G. Schirinzi. "A bayesian technique for In-SAR phase unwrapping." In Signal Recovery and Synthesis. OSA, 2001. http://dx.doi.org/10.1364/srs.2001.smd3.

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Baskaran, Shyamsunder, and R. P. Millane. "Bayesian Image Reconstruction in X-ray Fiber Diffraction." In Signal Recovery and Synthesis. Optica Publishing Group, 1998. http://dx.doi.org/10.1364/srs.1998.swa.3.

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The structure completion problem in x-ray fiber diffraction analysis, a crystallographic method for studying polymer structures, involves reconstructing an incomplete image from a known part and experimental data in the form of the squared amplitudes of the Fourier coefficients. Formulating this as a Bayesian estimation problem allows explicit expressions for MMSE and MAP estimates to be obtained. Calculations using simulated fiber diffraction data show that the MMSE estimate out- performs current methods that correspond to certain MAP estimates.

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Doerschuk, Peter C. "X-ray Crystallography as a Bayesian Signal Reconstruction Problem." In Signal Recovery and Synthesis. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/srs.1992.tuc1.

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A new Markov random field based algorithm is proposed for signal reconstruction from Fourier transform magnitude motivated by the data reduction calculations of x-ray crystallography. The purpose of an x-ray crystallography experiment is to determine the position in three dimensional space of each atom in a molecule. The measured data are the magnitudes squared of the Fourier transform of the electron density function of a crystal of the molecule of interest and possibly also of chemical derivatives. The data reduction calculations are a signal reconstruction problem for the three dimensional electron density. In the so-called “direct” methods of interest here, the reconstruction is based on a noisy measurement of the magnitude squared of the Fourier transform of the electron density of a single crystal, that is, no chemical derivatives of the molecule are studied.

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Wu, Chi-hsin, and Peter C. Doerschuk. "Markov random fields as a priori information for image restoration." In Signal Recovery and Synthesis. Optica Publishing Group, 1995. http://dx.doi.org/10.1364/srs.1995.rwc2.

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Markov random fields (MRFs) [1, 2, 3, 4] provide attractive statistical models for multidimensional signals. However, unfortunately, optimal Bayesian estimators tend to require large amounts of computation. We present an approximation to a particular Bayesian estimator which requires much reduced computation and an example illustrating low-light unknown-blur imaging. See [7] for an alternative approximation based on approximating the MRF lattice by a system of trees and for an alternative cost function.

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Maqbool, O., and H. A. Babri. "Bayesian Learning for Software Architecture Recovery." In 2007 International Conference on Electrical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icee.2007.4287309.

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Oh, S., A. B. Milstein, R. P. Millane, C. A. Boiiman, and K. J. Webb. "Three-dimensional Bayesian optical diffusion tomography with source-detector calibration." In Signal Recovery and Synthesis. OSA, 2001. http://dx.doi.org/10.1364/srs.2001.stua2.

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Chen, Chin-Tu, Valen E. Johnson, Wing H. Wong, Xiaoping Hub, and Charles E. Metz. "Statistical Methods for Image Restoration and Image Reconstruction." In Signal Recovery and Synthesis. Optica Publishing Group, 1989. http://dx.doi.org/10.1364/srs.1989.wd1.

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Zerafat, Mohammad Mehdi, Shahabbodin Ayatollahi, Nasir Mehranbod, and Davaood Barzegari. "Bayesian Network Analysis as a Tool for Efficient EOR Screening." In SPE Enhanced Oil Recovery Conference. Society of Petroleum Engineers, 2011. http://dx.doi.org/10.2118/143282-ms.

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